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# AI/ML Engineer

- **Company:** TAL International Container Corporation
- **Location:** Minnetonka, MN, United States
- **Experience:** Expert
- **Salary:** $80,000.0 - $105,000.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Automation of Tests, Big Data, Cloud Computing, Continuous Integration, Information Engineering, Information Leak Prevention, Distributed Computing Environment, Monitoring of Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Tensorflow, Azure Machine Learning, Software Engineering, Management of Software Versions, Supervised Learning, Feature Store, Pytorch, Retrieval-Augmented Generation, Large Language Models, Generative AI, Scikit Learn, Low Latency, Deployment Automation, Machine Learning Operations, Drift Detection, Restful APIs, GPT, Software Version Control, Devsecops, Docker, Unsupervised Learning, Databricks
- **Published:** September 30, 2026
- **Apply:** https://www.careerjet.com/job/us3a7d532eff636b1f94e8b1786c83eaed/eaa

## About the Role

Supervised Learning
Unsupervised Learning
NLP
Time-Series Forecasting
Statistical Analysis
Experience with large-scale data and production ML workloads.
Experience with scikit-learn and Databricks.
Strong analytical, quantitative, problem-solving, and critical-thinking skills.
NLP & LLM / GenAI
3+ years of production experience with NLP, including:
Transformers
GPT
Other modern NLP technologies
2-3 years of experience applying LLMs to real-world business problems.
Hands-on experience with:
RAG (Retrieval-Augmented Generation)
Vector Databases
Embeddings
Experience using vision and speech models in GenAI applications.
Experience working with large amounts of data for NLP and LLM solutions.
MLOps & Production Engineering
5+ years of software engineering experience.
2+ years of experience shipping ML models to production.
Strong understanding of MLOps and ML system design.
Experience with:
CI/CD
DevOps
Model deployment
Model monitoring
Model versioning
Understanding of ML production challenges, including:
Data leakage
Training-serving skew
Model drift
Performance degradation
Development & Cloud Technologies
Strong Python development skills.
Experience with ML frameworks:
TensorFlow
PyTorch
Experience with:
Docker
Kubernetes
REST APIs
Experience building APIs for machine learning models.
Experience with scalable model serving and distributed ML workloads.
Preferred / Nice-to-Have Skills
Experience with feature stores.
Experience with model registries.
Experience with model monitoring platforms.
GPU optimization experience.
Distributed training experience.
Responsible AI toolkits and compliance experience.
Soft Skills
Strong written and verbal communication skills.
Ability to present detailed technical analyses to broad audiences.
Organized, self-motivated, and able to work independently.
Strong analytical and problem-solving abilities.

## Description

Translate data science prototypes into production-grade ML services and pipelines.
Build training and inference code with:
Reproducibility
Versioning
Automated testing
Implement scalable online and offline model serving.
Optimize model serving for:
Batching
Latency
Throughput
Scalability
Integrate ML lifecycle tooling, including:
Experiment tracking
Model registries
Deployment automation
Model monitoring
Collaborate with Data Engineering teams on feature pipelines and data contracts.
Own production ML health, including:
Drift detection
Performance regression
Rollback strategies
Incident response, AI/ML Engineering
Python
TensorFlow
PyTorch
NLP
LLMs
RAG
Vector Databases
Embeddings
Databricks
scikit-learn
Docker
Kubernetes
REST APIs
MLOps
CI/CD
Digital Skills
Machine Learning
Natural Language Processing (NLP)
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